{"id":"W6901517604","doi":"10.6068/dp14ba80cb79442","title":"Trend 2002 - 2009. Statistics Canada. CANSIM: Environment - Natural Resources | Country: Canada | Table: Supply and demand of primary and secondary energy in natural units | Variable: Natural gas, primary energy, Total industrial | Units: Gigalitres, 2002-2009. Data-Planet™ Statistical Ready Reference by Conquest Systems, Inc. Dataset-ID: 075-001-086.","year":2015,"lang":"en","type":"other","venue":"Data Planet","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Economic statistics; Natural resource; Official statistics; Census; Natural (archaeology); Summary statistics; Descriptive statistics; Primary energy","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001835611,0.002397944,0.002370127,0.008021353,0.003096062,0.005086787,0.004627578,0.001379039,0.09899049],"category_scores_gemma":[0.01526716,0.001765438,0.002002898,0.04104847,0.0006580944,0.002756985,0.002277008,0.003032084,0.06169809],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.055795,"about_ca_system_score_gemma":0.1464064,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9947608,"about_ca_topic_score_gemma":0.9923279,"domain_scores_codex":[0.9958408,0.0002424519,0.0004205432,0.0004861815,0.002052495,0.0009574865],"domain_scores_gemma":[0.9683093,0.0009452642,0.0008070478,0.0008908444,0.02765474,0.001392793],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00001826507,0.000005313811,0.0008196283,0.0001940208,0.00001690122,0.000006777948,0.00001731878,0.0001197284,0.00001009556,0.0004543494,0.9968059,0.001531639],"study_design_scores_gemma":[0.00009589483,0.000008240065,0.01583691,0.0005901504,0.00004523698,0.00002212164,0.0003653883,0.0004608073,0.0001729206,0.0006465988,0.9816836,0.00007213368],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00004777872,0.0000481093,0.00003020703,0.000141158,0.00003526844,0.00001424272,0.9982436,0.00007461126,0.001365103],"genre_scores_gemma":[0.0009868453,0.0003390389,0.0005202799,0.0001901742,0.00001993221,0.0001249019,0.9914007,0.0001655722,0.006252577],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.09899049,"threshold_uncertainty_score":0.4048229,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01411879313569574,"score_gpt":0.1985855425235692,"score_spread":0.1844667493878734,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}